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Updated: Feb 24, 2026
Real-Time fMRI Brain Mapping in Animals
Published on: September 24, 2020
Real-time motion analytics during brain MRI improve data quality and reduce costs
Nico U F Dosenbach1, Jonathan M Koller2, Eric A Earl3
1Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA; Program in Occupational Therapy, Washington University, St. Louis, MO, USA.
Abstract:
Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.
Insights
Head motion significantly impacts MRI data quality. Framewise Integrated Real-time MRI Monitoring (FIRMM) software helps collect sufficient high-quality brain MRI data, reducing scan times and costs by over 50%.
Area of Science:
- Neuroimaging
- Medical Physics
- Biomedical Engineering
Background:
- Head motion is a significant confounder in structural and functional brain MRI studies.
- Excluding motion-corrupted data frames can lead to substantial data loss, especially in pediatric cohorts.
- Current methods like collecting buffer data are costly and do not guarantee sufficient high-quality data.
Purpose of the Study:
- To develop and evaluate an easy-to-use software suite for real-time head motion monitoring during MRI scans.
- To enable the collection of sufficient high-quality MRI data while minimizing scan time and costs.
- To provide scanner operators with real-time motion analytics for adaptive data acquisition.
Main Methods:
- Development of the Framewise Integrated Real-time MRI Monitoring (FIRMM) software.
- Implementation of real-time head motion analytics for scanner operators.
- Utilizing FIRMM to determine optimal scan duration based on motion criteria for individual participants.
Main Results:
- FIRMM software provides real-time head motion analytics.
- The software allows for adaptive scanning until desired low-movement data is acquired.
- Utilizing FIRMM reduced total brain MRI scan times and associated costs by 50% or more.
Conclusions:
- Real-time motion monitoring with FIRMM is an effective strategy to improve MRI data quality.
- FIRMM facilitates efficient data acquisition, reducing scan time and costs in clinical and research settings.
- This approach offers a practical solution to mitigate motion artifacts in brain MRI, particularly for challenging populations.
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